Pre-COVID-19-pandemic RSV epidemiology and clinical burden in pediatric primary care in Italy: a comparative analysis across two regions for the 2019/2020 season.

BACKGROUND: Respiratory syncytial virus (RSV) infection in children under 5 years have a significant clinical burden, also in primary care settings. This study investigates the epidemiology and burden of RSV in Italian children during the 2019/20 pre-pandemic winter season. METHODS: A prospective cohort study was conducted in two Italian regions. Children with Acute Respiratory Infection (ARI) visiting pediatricians were eligible. Nasopharyngeal swabs were collected and analyzed via multiplex PCR for RSV detection. A follow-up questionnaire after 14 days assessed disease burden, encompassing healthcare utilization and illness duration. Statistical analyses, including regression models, explored associations between variables such as RSV subtype and regional variations. RESULTS: Of 293 children with ARI, 41% (119) tested positive for RSV. Median illness duration for RSV-positive cases was 7 days; 6% required hospitalization (median stay: 7 days). Medication was prescribed to 95% (110/116) of RSV cases, with 31% (34/116) receiving antibiotics. RSV subtype B and regional factors predicted increased healthcare utilization. Children with shortness of breath experienced a 36% longer illness duration. CONCLUSIONS: This study highlights a significant clinical burden and healthcare utilization associated with RSV in pre-pandemic Italian primary care settings. Identified predictors, including RSV subtype and symptomatology, indicate the need for targeted interventions and resource allocation strategies. RSV epidemiology can guide public health strategies for the implementation of preventive measures.

A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability.

Introduction: Europe works to improve cancer management through the use of artificialintelligence (AI), and there is a need to accelerate the development of AI applications for childhood cancer. However, the current strategies used for algorithm development in childhood cancer may have bias and limited generalizability. This study reviewed existing publications on AI tools for pediatric brain tumors, Europe's most common type of childhood solid tumor, to examine the data sources for developing AI tools. Methods: We performed a bibliometric analysis of the publications on AI tools for pediatric brain tumors, and we examined the type of data used, data sources, and geographic location of cohorts to evaluate the generalizability of the algorithms. Results: We screened 10503 publications, and we selected 45. A total of 34/45 publications developing AI tools focused on glial tumors, while 35/45 used MRI as a source of information to predict the classification and prognosis. The median number of patients for algorithm development was 89 for single-center studies and 120 for multicenter studies. A total of 17/45 publications used pediatric datasets from the UK. Discussion: Since the development of AI tools for pediatric brain tumors is still in its infancy, there is a need to support data exchange and collaboration between centers to increase the number of patients used for algorithm training and improve their generalizability. To this end, there is a need for increased data exchange and collaboration between centers and to explore the applicability of decentralized privacy-preserving technologies consistent with the General Data Protection Regulation (GDPR). This is particularly important in light of using the European Health Data Space and international collaborations.

Prioritizing educational initiatives on emerging technologies for Italian pediatricians: bibliometric review and a survey.

BACKGROUND: Emerging technologies have demonstrated outstanding potential in improving healthcare, yet their full integration remains a challenge for all medical specialties, including pediatrics. To support the swift implementation of technologies, we identified the current trends through a bibliometric review, and we conducted a survey on Italian pediatricians to gauge educational needs and willingness to integrate technologies into clinical practice. METHODS: A working group of pediatricians representing various backgrounds designed and coordinated the study. To identify relevant topics for educational strategy development, we focused on virtual reality, telehealth, natural language processing, smartphone applications, robotics, genomics, and artificial intelligence. A bibliometric analysis limited to 2018-2023 was performed to identify trends and emerging applications within each topic. Based on the results, a questionnaire was developed and made available online to all Italian pediatricians. The results were analyzed through descriptive analysis and a multivariable logistic regression to explore associations between technology adoption and sociodemographic characteristics. RESULTS: A total of 3,253 publications were found, with Telehealth and Telemedicine having the highest number of publications and Natural Language Processing the lowest. The number of respondents to the online questionnaire was 1,540, predominantly medical doctors with over 20 years of experience working as family pediatricians. Telehealth had the highest level of knowledge (95.2%), followed by smartphone applications (89.1%) and genomics (63.2%). The greatest potential for increased use through education programs was projected for natural language processing (+ 43.1%), artificial intelligence (+ 39.6%), and virtual and mixed reality (+ 38.1%). Female respondents and older individuals were less likely to use emerging technologies. Hospital pediatricians and residents were more likely to use AI. CONCLUSIONS: We developed a replicable strategy to identify emerging themes in medical technologies relevant to pediatrics and assess the educational needs of pediatricians. A significant gap still exists between current and potential usage of emerging technologies among Italian pediatricians although they showed a positive attitude towards implementing these technologies following specific education programs. The study highlights the need for comprehensive education programs on emerging technologies in pediatrics and recommends addressing gender and age disparities in technology adoption.

Autosomal Recessive Long QT Syndrome: Clinical Aspects and Therapy.

The autosomal recessive (AR) form of Long QT Syndrome (LQTS) is described both associated with deafness known as Jervell and Lange-Nielsen (JLN) syndrome, and without deafness (WD). The aim of the study is to report the characteristics of AR LQTS patients and the efficacy of the therapy. Data of all children with AR LQTS referred to the Bambino Gesù Children's Hospital IRCCS from September 2012 to September 2021were included. Three (30%) patients had compound heterozygosity and 7 (70%) had homozygous variants of the KCNQ1 gene, the latter showing deafness. Four patients (40%) presented aborted sudden cardiac death (aSCD): three with previous episodes of syncope (75%), the other without previous symptoms (16.6% of asymptomatic patients). An episode of aSCD occurred in 2/3 (66.7%) of WD and heterozygous patients, while in 2/7 (28%) JLN and homozygous patients and in 2/2 patients with QTC > 600 ms. All patients were treated with Nadolol. In 5 Mexiletine was added, shortening QTc and obtaining the disappearance of the T-wave alternance (TWA) in 3/3. Episodes of aSCD seem to be more frequent in LQTS patients with compound heterozygous variants and WD than in those with JLN and homozygous variants. Episodes of aSCD also appear more frequent in children with syncope or with QTc value > 600 ms, even on beta-blocker therapy, than in patients without syncope or with Qtc < 600 ms. However, our descriptive results should be confirmed by larger studies. Moreover, Mexiletine addition reduced QTc value and eliminated TWA.

Current practices of follow-up programs for childhood cancer survivors in Italy.

INTRODUCTION: Quality of life in childhood cancer survivors is largely affected by survivorship care and transition from treatment to long-term follow-up (LTFU). Referring to evidence-based recommendations, we wanted to evaluate LTFU care for survivors through a survey among the Italian Association for Pediatric Hematology-Oncology (AIEOP) centers. The project aimed to evaluate the availability of services in Italy, investigate strengths and weaknesses, analyze improvements of awareness in the field, and identify the gaps that need to be addressed by different centers. METHODS: Together with the family representatives, on behalf of AIEOP's Late Effects Working Group, we developed a questionnaire on assisting childhood cancer survivors. All AIEOP centers received one questionnaire including information on local health system organizations; LTFU for childhood cancer survivors; services for adult survivors of childhood cancer; information provided to survivors/caregivers and care plan delivery. RESULTS: Forty-eight AIEOP centers were contacted and 42 replied, with a response rate of 87.5%. The majority of respondents confirmed their interest in assisting patients with a survivorship care plan (95.2%), regardless of a clinic or dedicated staff. DISCUSSION: This is the first overview of LTFU in Italy, which provides detailed results at national levels, prompting consideration of improvements in the last decade. Although there is a high level of interest in survivorship care, many centers lack resources to implement such programs. The identification of these challenges is useful for planning future strategies.

Adherence to a Mediterranean Diet, Body Composition and Energy Expenditure in Outpatients Adolescents Diagnosed with Anorexia Nervosa: A Pilot Study.

Weight restoration is the primary goal of treatment for patients with Anorexia Nervosa (AN). This observational pilot study aims to describe adherence to the Mediterranean Diet (MD) and the consequent process of weight and functional recovery in outpatient adolescents diagnosed with AN. Eight patients with a median age of 15.1 (14.0-17.1) years were seen at baseline and after six months. Anthropometrics, body composition, and resting energy expenditure (REE) were assessed. The KIDMED questionnaire, the 24 h recall, and a quantitative food frequency questionnaire were used to evaluate adherence to the MD. The median KIDMED score increased from 5.5 (T0) to 10 (T1), which was not significant. Intakes of grams of carbohydrates, lipids, mono-unsaturated fatty acids, and fiber increased (p = 0.012, p = 0.036, p = 0.036, p = 0.025). Weight significantly increased (p = 0.012) as well as lean body mass (p = 0.036), with a resulting improvement of the REE (p = 0.012). No association between anthropometrics and body composition and the KIDMED score was found. The MD could represent an optimal dietary pattern for weight gain and nutritional restoration in patients with AN, and it could lead to an improvement in body composition and resting energy expenditure.

Frequency and characterization of cognitive impairments in patients diagnosed with paediatric central nervous system tumours: a systematic review.

Background: This systematic review has been conducted with the aim of characterizing cognitive deficits and analyzing their frequency in survivors of paediatric Central Nervous System tumours. Materials and methods: All literature published up to January 2023 was retrieved searching the databases "PubMed", "Cochrane", "APA PsycInfo" and "CINAHL". The following set of pre-defined inclusion criteria were then individually applied to the selected articles in their full-text version: i) Retrospective/prospective longitudinal observational studies including only patients diagnosed with primary cerebral tumours at ≤ 21 years (range 0-21); ii) Studies including patients evaluated for neuro-cognitive and neuro-psychological deficits from their diagnosis and/or from anti-tumoral therapies; iii) Studies reporting standardized tests evaluating patients' neuro-cognitive and neuro-psychological performances; iv) Patients with follow-ups ≥ 2 years from the end of their anti-tumoral therapies; v) Studies reporting frequencies of cognitive deficits. Results: 39 studies were included in the analysis. Of these, 35 assessed intellectual functioning, 30 examined memory domains, 24 assessed executive functions, 22 assessed attention, 16 examined visuo-spatial skills, and 15 explored language. A total of 34 studies assessed more than one cognitive function, only 5 studies limited their analysis on a single cognitive domain. Attention impairments were the most recurrent in this population, with a mean frequency of 52.3% after a median period post-treatment of 11.5 years. The other cognitive functions investigated in the studies showed a similar frequency of impairments, with executive functions, language, visuospatial skills and memory deficits occurring in about 40% of survivors after a similar post-treatment period. Longitudinal studies included in the systematic review showed a frequent decline over time of intellectual functioning. Conclusions: Survivors of paediatric Central Nervous System tumours experience cognitive sequelae characterized by significant impairments in the attention domain (52.3%), but also in the other cognitive functions. Future studies in this research field need to implement more cognitive interventions and effective, but less neurotoxic, tumour therapies to preserve or improve neurocognitive functioning and quality of life of this population.

Machine learning clinical decision support systems for surveillance: a case study on pertussis and RSV in children.

We tested the performance of a machine learning (ML) algorithm based on signs and symptoms for the diagnosis of RSV infection or pertussis in the first year of age to support clinical decisions and provide timely data for public health surveillance. We used data from a retrospective case series of children in the first year of life investigated for acute respiratory infections in the emergency room from 2015 to 2020. We collected data from PCR laboratory tests for confirming pertussis or RSV infection, clinical symptoms, and routine blood testing results, which were used for the algorithm development. We used a LightGBM model to develop 2 sets of models for predicting pertussis and RSV infection: for each type of infection, we developed one model trained with the combination of clinical symptoms and results from routine blood test (white blood cell count, lymphocyte fraction and C-reactive protein), and one with symptoms only. All analyses were performed using Python 3.7.4 with Shapley values (Shap values) visualization package for predictor visualization. The performance of the models was assessed through confusion matrices. The models were developed on a dataset of 599 children. The recall for the pertussis model combining symptoms and routine laboratory tests was 0.72, and 0.74 with clinical symptoms only. For RSV infection, recall was 0.68 with clinical symptoms and laboratory tests and 0.71 with clinical symptoms only. The F1 score for the pertussis model was 0.72 in both models, and, for RSV infection, it was 0.69 and 0.75. ML models can support the diagnosis and surveillance of infectious diseases such as pertussis or RSV infection in children based on common symptoms and laboratory tests. ML-based clinical decision support systems may be developed in the future in large networks to create accurate tools for clinical support and public health surveillance.

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health.

Large Language Models (LLMs) have recently gathered attention with the release of ChatGPT, a user-centered chatbot released by OpenAI. In this perspective article, we retrace the evolution of LLMs to understand the revolution brought by ChatGPT in the artificial intelligence (AI) field. The opportunities offered by LLMs in supporting scientific research are multiple and various models have already been tested in Natural Language Processing (NLP) tasks in this domain. The impact of ChatGPT has been huge for the general public and the research community, with many authors using the chatbot to write part of their articles and some papers even listing ChatGPT as an author. Alarming ethical and practical challenges emerge from the use of LLMs, particularly in the medical field for the potential impact on public health. Infodemic is a trending topic in public health and the ability of LLMs to rapidly produce vast amounts of text could leverage misinformation spread at an unprecedented scale, this could create an "AI-driven infodemic," a novel public health threat. Policies to contrast this phenomenon need to be rapidly elaborated, the inability to accurately detect artificial-intelligence-produced text is an unresolved issue.

Psychopathological Profile Associated with Food Addiction Symptoms in Adolescents with Eating Disorders.

Eating disorders are considered one of the psychiatric disorders with a higher risk of death. Food addiction, related to some food addictive-like behaviours, is often in comorbidity with eating disorders and is associated with worse psychopathology. The present study aims to outline the food addiction profile, investigated using the Yale Food Addiction Scale 2.0 (YFAS 2.0), in 122 adolescents (median age: 15.6 years) suffering from eating disorders and to investigate its association with psychopathology. Patients filled out the Youth Self Report, the Multidimensional Anxiety Scale for Children 2, The Children Depression Inventory 2, and the Eating Disorder Inventory 3 (EDI-3). Pearson's chi-square test and multiple correspondence analysis were used to identify profiles. The mean symptom count was 2.8 ± 2.7. The "withdrawal" symptom was the most frequent (51%) and the most associated with clinical scores. The diagnosis of bulimia nervosa and the EDI-3 bulimia scale resulted to be the only variables to be associated with positive YFAS 2.0 symptoms. Conversely, anorexia nervosa, restrictive and atypical, was not associated with YFAS 2.0 symptoms. In conclusion, outlining the food addiction profile of eating disorders may give information about a patient's phenotype and could help to identify specific treatment models.

Public Engagement in Digital Recommendations for Promoting Healthy Parental Behaviours from Preconception through the First 1000 Days.

Web-based digital interventions may play a central role for health promoting strategies in the first "1000 days", from conception through the first 2 years of life. We developed a web platform providing evidence-based recommendations in the first 1000 days through short videos, and we studied engagement by users from preconception through parenthood in the second year of life. We described the access to videos by topic and used a multilevel model to explore the user characteristics associated with access to the video recommendations. Overall, breastfeeding, physical activity and nutrition were the most popular topics (normalized views: 89.2%, 87.2% and 86.4% respectively), while content on paternal health and smoking and alcohol was less engaging (37.3% and 42.0%). Nutrition content was the most viewed in the preconception period and during the first two trimesters of pregnancy. Nutrition and breastfeeding were also the most popular topics for users with children less than 2 years old. Higher levels of health literacy were associated only with child health content. The study findings indicate that digital strategies should be adapted according to the time period in the first 1000 days. Alternative digital promotion strategies for the less engaging topics should be considered.

A simulation model estimates lifetime health and economic outcomes of screening prediabetes using the 1-h plasma glucose.

AIMS: The current method to diagnose impaired glucose tolerance (IGT) is based on the 2-h plasma glucose (2-hPG) value during a 75-g oral glucose tolerance test (OGTT). Robust evidence demonstrates that the 1-h post-load plasma glucose (1-hPG) ≥ 8.6 mmol/L in those with normal glucose tolerance is highly predictive of type 2 diabetes (T2D), micro and macrovascular complications and mortality. The aim of this study was to conduct a health economic analysis to estimate long-term cost-effectiveness of using the 1-hPG compared to the 2-hPG for screening and assessing the risk of diabetes over 35 years. The main outcome was cost per quality-adjusted life year (QALY) gained. METHODS: A Monte Carlo-based Markov simulation model was developed to forecast long-term effects of two screening strategies with regards to clinical and cost-effectiveness outcomes. The base case model included 20,000 simulated patients over 35-years follow-up. Transition probabilities on disease progression, mortality, effects on preventive treatments and complications were retrieved from landmark diabetes studies. Direct medical costs were sourced from published literature and inflated to 2019 Euros. RESULTS: In the lifetime analysis, the 1-hPG was projected to increase the number of years free from disease (2 years per patient); to delay the onset of T2D (1 year per patient); to reduce the incidence of T2D complications (0·6 RR-Relative Risk per patient) and to increase the QALY gained (0·58 per patient). Even if the 1-hPG diagnostic method resulted in higher initial costs associated with preventive treatment, long-term diabetes-related costs as well as complications costs were reduced leading to a lifetime saving of - 31225719.82€. The incremental cost-effectiveness ratio was - 8214.7€ per each QALY gained for the overall population. CONCLUSIONS: Screening prediabetes with the 1-hPG is feasible and cost-effective resulting in reduced costs per QALY. Notwithstanding, the higher initial costs of testing with the 1-hPG compared to the 2-hPG due to incremental preventive intervention, long-term diabetes and complications costs were reduced projecting an overall cost saving of - 8214.7€ per each QALY gained.

The awareness and acceptance of anti-COVID 19 vaccination in adolescence.

BACKGROUND: COVID-19 had devastating effects on children's and adolescents' life, including neuropsychological impairment, discontinuation of social life and education. Since June 2021, antiCOVID19 vaccination has become available to adolescents in Italy up to 12 years and since December 2021 to children aged more than 5 years. The pediatric population represents a challenging target for vaccination. Aim of the study is to perform a survey among adolescents to explore factors associated with COVID 19 immunization and their perceptions about COVID-19 vaccines. METHODS: Italian students aged 10-17 years were invited to participate in an anonymous online survey regarding their immunization against COVID-19 and their opinion on the immunization practice through a web link to the questionnaire. The study period was March-June 2022. Statistical analysis was performed with SPSS v 21. RESULTS: In the study period, 895 students entered the survey. A total of 87.3% of respondents were immunized against SARS-CoV2. The most important predictors of being immunized against SARS-CoV2 were having both parents immunized (p < 0, 001) and being aged over 12 years. In the unvaccinated group, the decision was mostly influenced by the family (65.8%). Regardless the immunization status, respondents were willing to receive information about COVID 19 vaccination mostly by their family doctor (51.8%) and at school (28.9%). CONCLUSIONS: Parents' decisions and attitudes strongly affected the immunization status of adolescents. Students' willing to receive COVID 19 vaccine information by family doctors and at school, underline the potential role of paediatricians and school educators in contributing to an increased vaccine coverage among the paediatric age.

Feasibility of a VR Intervention to Decrease Anxiety in Children with Tumors Undergoing CVC Dressing.

Virtual reality (VR) represents a promising digital intervention for managing distress and anxiety in children with tumors undergoing painful medical procedures. In an experimental cross-over study, we administered a VR intervention consisting of relaxing games during central venous catheter (CVC) dressing. The VR sessions were compared with no-VR during CVC medication. We used the distress thermometer and RCMAS-2 scale to assess distress and anxiety levels. We also explored the satisfaction level in patients and families. We enrolled 22 children. The distress levels after medication were lower in the VR group than in those without VR (VR: median 2; IQR 0-2; no-VR: median 4; IQR: 3-5). No variation in anxiety levels was detected by VR intervention. Satisfaction for using VR was very high in children and their families although a total of 12% of children were disappointed by the effect of VR. Most healthcare workers felt that VR would be useful in routine clinical practice. A VR intervention is highly acceptable, may be efficacious in decreasing distress in children with cancer undergoing painful procedures, but it is less likely that it has a measurable impact on anxiety. Evidence from larger studies is needed to assess VR translation into the clinical workflow.

Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model.

Social media is increasingly being used to express opinions and attitudes toward vaccines. The vaccine stance of social media posts can be classified in almost real-time using machine learning. We describe the use of a Transformer-based machine learning model for analyzing vaccine stance of Italian tweets, and demonstrate the need to address changes over time in vaccine-related language, through periodic model retraining. Vaccine-related tweets were collected through a platform developed for the European Joint Action on Vaccination. Two datasets were collected, the first between November 2019 and June 2020, the second from April to September 2021. The tweets were manually categorized by three independent annotators. After cleaning, the total dataset consisted of 1,736 tweets with 3 categories (promotional, neutral, and discouraging). The manually classified tweets were used to train and test various machine learning models. The model that classified the data most similarly to humans was XLM-Roberta-large, a multilingual version of the Transformer-based model RoBERTa. The model hyper-parameters were tuned and then the model ran five times. The fine-tuned model with the best F-score over the validation dataset was selected. Running the selected fine-tuned model on just the first test dataset resulted in an accuracy of 72.8% (F-score 0.713). Using this model on the second test dataset resulted in a 10% drop in accuracy to 62.1% (F-score 0.617), indicating that the model recognized a difference in language between the datasets. On the combined test datasets the accuracy was 70.1% (F-score 0.689). Retraining the model using data from the first and second datasets increased the accuracy over the second test dataset to 71.3% (F-score 0.713), a 9% improvement from when using just the first dataset for training. The accuracy over the first test dataset remained the same at 72.8% (F-score 0.721). The accuracy over the combined test datasets was then 72.4% (F-score 0.720), a 2% improvement. Through fine-tuning a machine-learning model on task-specific data, the accuracy achieved in categorizing tweets was close to that expected by a single human annotator. Regular training of machine-learning models with recent data is advisable to maximize accuracy.

How the Italian Twitter Conversation on Vaccines Changed During the First Phase of the Pandemic: A Mixed-Method Analysis.

In the context of the European Joint Action on Vaccination, we analyzed, through quantitative and qualitative methods, a random sample of vaccine-related tweets published in Italy between November 2019 and June 2020, with the aim of understanding how the Twitter conversation on vaccines changed during the first phase of the pandemic, compared to the pre-pandemic months. Tweets were analyzed by a multidisciplinary team in terms of kind of vaccine, vaccine stance, tone of voice, population target, mentioned source of information. Multiple correspondence analysis was used to identify variables associated with vaccine stance. We analyzed 2,473 tweets. 58.2% mentioned the COVID-19 vaccine. Most had a discouraging stance (38.1%), followed by promotional (32.5%), neutral (22%) and ambiguous (2.5%). The discouraging stance was the most represented before the pandemic (69.6%). In February and March 2020, discouraging tweets decreased intensely and promotional and neutral tweets dominated the conversation. Between April and June 2020, promotional tweets remained more represented (36.5%), followed by discouraging (30%) and neutral (24.3%). The tweets' tone of voice was mainly polemical/complaining, both for promotional and for discouraging tweets. The multiple correspondence analysis identified a definite profile for discouraging and neutral tweets, compared to promotional and ambiguous tweets. In conclusion, the emergence of SARS-CoV-2 caused a deep change in the vaccination discourse on Twitter in Italy, with an increase of promotional and ambiguous tweets. Systematic monitoring of Twitter and other social media, ideally combined with traditional surveys, would enable us to better understand Italian vaccine hesitancy and plan tailored, data-based communication strategies.

Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis.

The application of artificial intelligence (AI) systems is emerging in many fields in recent years, due to the increased computing power available at lower cost. Although its applications in various branches of medicine, such as pediatric oncology, are many and promising, its use is still in an embryonic stage. The aim of this paper is to provide an overview of the state of the art regarding the AI application in pediatric oncology, through a systematic review of systematic reviews, and to analyze current trends in Europe, through a bibliometric analysis of publications written by European authors. Among 330 records found, 25 were included in the systematic review. All papers have been published since 2017, demonstrating only recent attention to this field. The total number of studies included in the selected reviews was 674, with a third including an author with a European affiliation. In bibliometric analysis, 304 out of the 978 records found were included. Similarly, the number of publications began to dramatically increase from 2017. Most explored AI applications regard the use of diagnostic images, particularly radiomics, as well as the group of neoplasms most involved are the central nervous system tumors. No evidence was found regarding the use of AI for process mining, clinical pathway modeling, or computer interpreted guidelines to improve the healthcare process. No robust evidence is yet available in any of the domains investigated by systematic reviews. However, the scientific production in Europe is significant and consistent with the topics covered in systematic reviews at the global level. The use of AI in pediatric oncology is developing rapidly with promising results, but numerous gaps and challenges persist to validate its utilization in clinical practice. An important limitation is the need for large datasets for training algorithms, calling for international collaborative studies.

Using the Social Robot NAO for Emotional Support to Children at a Pediatric Emergency Department: Randomized Clinical Trial.

BACKGROUND: Social robots (SRs) have been used for improving anxiety in children in stressful clinical situations, such as during painful procedures. However, no studies have yet been performed to assess their effect in children while waiting for emergency room consultations. OBJECTIVE: This study aims to assess the impact of SRs on managing stress in children waiting for an emergency room procedure through the assessment of salivary cortisol levels. METHODS: This was an open randomized clinical trial in children attending a pediatric emergency department. Children accessing the emergency room were randomized to 1 of 3 groups: (1) playing with a NAO SR, (2) playing with a study nurse, or (3) waiting with parents. The salivary cortisol levels of all children were measured through a swab. Salivary cortisol levels before and after the intervention were compared in the 3 groups. We calculated the effect size of our interventions through the Cohen d-based effect size correlation (r). RESULTS: A total of 109 children aged 3-10 years were enrolled in the study, and 94 (86.2%) had complete data for the analyses. Salivary cortisol levels significantly decreased more in the group exposed to robot interaction than in the other two groups (r=0.75). Cortisol levels decreased more in girls (r=0.92) than in boys (r=0.57). CONCLUSIONS: SRs are efficacious in decreasing stress in children accessing the emergency room and may be considered a tool for improving emotional perceptions of children and their families in such a critical setting. TRIAL REGISTRATION: ClinicalTrials.gov NCT04627909; https://clinicaltrials.gov/ct2/show/study/NCT04627909.

Intention of Parents to Immunize Children against SARS-CoV-2 in Italy.

Several countries have targeted adolescents for immunization against SARS-CoV-2 to mitigate COVID-19 spread. In Italy, immunization for children ≥ 12 years has been available starting from June 2021. We conducted a cross-sectional study to investigate the knowledge, attitude and intention to vaccinate children < 18 years in Italian families. We used a multinomial logistic regression model to investigate factors associated with intention to vaccinate. We collected a total of 1696 responses. Among the 491 families of children ≥ 12 years, 41.2% would not vaccinate their children and 21.2% were uncertain, while among the 1205 families of children < 12 years, 36.1% would not vaccinate and 33.8% were uncertain. Determinants of intention to vaccinate both age groups were perceived safety and efficacy of vaccines and perceived risk of transmitting infection to adults. For children < 12 years, additional determinants were perceived risk of being infected and being hospitalized because of COVID-19. In view of the expanding strategy to vaccinate adolescents and the availability of immunization for children < 12 years, our results call for a communication strategy targeted at families of children focused on the safety and efficacy of COVID-19 vaccine in children and on the dynamics of infection spread across different age groups. As perceptions in families are volatile and may change rapidly over time, repeated surveys for measuring attitudes to vaccinate would be advisable.